Data processing systems have been in ever-increasing use over the past half century. One challenge facing operators and system administrators of such systems is how to detect and diagnose performance problems with the system before such problems reach a critical stage and cause a system failure. Numerous patents have addressed the problem of performance monitoring of various components or processes within a computer system. Some disclose processes of taking affirmative actions, such as band-width throttling, to adjust the resources of a system. For example, U.S. Pat. No. 5,732,240 discloses a technique for real-time adjustment of cache size in a computer system. However, few, if any, attempts have been made to diagnose the status of a process or system and make recommendations to a system administrator on how such problems may be resolved bases on the current health thereof. This is particularly true with more sophisticated processes such as server processes coupled to a computer network.
One of the impediments to designers of such systems has been the inability to convert the large amount of data relating to the status of a system into a meaningful recommendation which accurately identifies the source of a problem. Accordingly, a need exists for a technique in which data representing the status of a system can be analyzed and a recommendation generated for resolving new problems reflected in the data.
In addition, a further impediment to designers of diagnostic systems is the need to compensate for differences in system resources and platform configurations. For example, the hardware configuration and operating system performance have a direct influence on the performance of a server application, particularly with resource considerations such as available memory, processor speed, and network interface bandwidth. In addition, other dynamic factors may influence the performance of a process, such as the number of other processes simultaneously executing on the same system. Accordingly, a need exists for a technique in which performance criteria may be meaningfully applied to a plurality of processes executing on different platforms and in different load environments. A further need exists for a technique both compensates for such differences and can generate accurate diagnostic recommendations based on the disparate data collected among a plurality of processes.
In addition, once data has been collected about the status of a plurality of different processes it is often difficult to display such data in a format that allows a system administrator to easily track the status of a plurality of monitored processes and to understand any accompanying diagnostic recommendations. Accordingly, a further need exists for a technique in which status data and diagnostic recommendations for a plurality of different processes are displayed in a format that allows a system administrator to easily understand.